Thread by @EdenEmarco177

🧵🚀 Following my last thread on "in-context learning", now it's time to explain how we can digest our custom data so that LLM’s 🤖 can use it. Spoiler alert- @LangChainAI 🦜 🔗 and a vector store like @pinecone 🌲 will do all the work for us.

Link:

https://twitter.com/EdenEmarco177/status/1659558174121250816

1/12 This is a laser focused thread 🧵 for devs and software engineers. Even if you have zero AI knowledge (like I did just 6 months ago)- I will be simplifying key data concepts for any gen ai application💡

2/12 Let's talk custom data digestion for LLMs 🤖
First off: Embedding models. These condense complex data into meaningful vectors, capturing relationships and semantic meaning. Think of it as a black box for text ➡ vector conversion. (vector = list of floats)

3/11 There are many embedding models, e.g @googlecloud ☁️ VertexAI Embeddings. Each has its pros and cons, considering cost, storage latency, and other factors. The main idea: transform data into vectors representing semantic meaning.

4/12 It is important to note that semantically related vectors will be “located” close to each in the vector space.
How does this work? Well…. a lot of math that I don’t really care about as a dev lol 😂 just use black box and let google vertex do its magic for me! 🎩

5/12 Now, if we take all the HTML documentation of a Python package (like @LangChainAI ) and embed it, we end up with a bunch of vectors, each representing a different doc page. If we used @googlecloud ☁️ VertexAI embeddings, the vectors' size = dimension would be 768.

6/12 So what do we do with these vectors? On their own, there isn't much we can do. They're just lists of numbers. But that's where a cloud-based ☁️ managed vectorstore like @pinecone 🌲 comes in, which saves us from storing hundreds of GBs of vectors on our own machines!

7/12 In @pinecone 🌲, we can create an index with the same dimension as our embeddings model (768 in this example). Then, we just need to iterate over the vectors we got back from the embedding model and upsert them into the vector store. Simple! 👌

8/12 Vectorstores like @pinecone🌲 offer semantic search functionality to find vectors close to our query vector. These semantically related vectors hold the info our LLM needs to answer.

9/12 How does this semantic search work? Complex algorithms, heavy math calculations which again I don’t really want to know as lazy dev, thank you @pinecone engineers for abstracting this for me! 🙏

10/12 Enters now @LangChainAI 🦜 🔗. This open source framework automates this entire process, doing the heavy lifting for us. With just one line of code, you can invoke a function that handles data embedding, and inserts all created vectors into @pinecone . Easy as pie! 🥧

11/12 @LangChainAI 🦜 🔗 has so much more to offer, making our lives easier when developing production grade LLM powered applications. IMO, it's the go-to open-source framework for developing such apps. Check out their docs here: python.langchain.com/en/latest/

12/12
Ready to dive in the entire code? Check out the Github repo -
github.com/emarco177/docu…

Inspired by @hwchase17, the creator of @LangChainAI.
Happy coding, everyone! 💻🚀
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